Deep Learning methodology using gradient descent

Usual Deep Learning methodology to build the model:

1. Initialize parameters / Define hyperparameters
2. Loop for num_iterations:   

                       a. Forward propagation 

                       b. Compute cost function 

                       c. Backward propagation 

                       d. Update parameters (using parameters, and grads from backprop)

    3. Use trained parameters to predict labels